AI & Machine Learning
What makes AI projects succeed: the leap from PoC to production.
In AI projects, the idea and the demo stage usually move fast; the real difference shows up in the leap from PoC to production. This is exactly where most efforts stall — data quality falls short, the MLOps stack was never built, hallucination risk goes unmanaged, and token costs spiral. What determines the outcome isn't the model itself — it's the discipline that carries it to production, and that's exactly where we focus.
At Avva Mobile, AI projects start with use-case discovery and a data-readiness assessment. Model build or fine-tuning is framed against the production target from the very start; monitoring, retraining, and cost control are part of the architecture, not a patch bolted on later. You work with teams certified on AWS Bedrock, Vertex AI, and Azure AI Foundry.
The practical outcome: systems that typically go from PoC to production within 8 weeks, a measurable drop in LLM token cost with the right design, and monitoring wired from day one. Not a hallucination-prone demo — a measurable, safe, and sustainable AI system.
Use case to production. Every step deliberate.
Use Case & Data Readiness
We clarify which problem is worth solving with AI and tie the expected business value to measurable targets. We assess your data sources, their quality, and their accessibility — making the path to production visible before work even begins. Skipping this phase is the most expensive debt you'll pay in production.
Model Build & Fine-Tune
We pick the approach that fits the problem: for LLM-based solutions, RAG, agents, or fine-tuning; as needed, computer vision, NLP, recommendation, or forecasting models. We share model performance with you at each iteration and carry feedback straight into the next cycle. We constrain hallucination risk by design from the start.
MLOps & Validation
Before moving the model to production, we put it through systematic evaluation: eval sets, guardrails, and A/B tests. The MLOps pipeline is built — versioning, retraining triggers, and rollback mechanisms are wired. When you go live, model behavior is no surprise.
Ship & Continuous Monitoring
We deploy the model on edge or cloud — both, as needed. We ship the user-facing feature live and switch on 100% monitoring and alerting. Model drift, token cost, and latency are tracked continuously; cost discipline is a lasting cycle, not a one-off.
WHAT SETS US APART
The difference shows in production.
The common approach.
- Impressive demo, unclear production
- MLOps as an afterthought
- Hallucination risk unmanaged
- Token cost unpredictable
- Monitoring usually missing
The Avva Mobile approach.
- Typically 8 weeks PoC to production
- Measured token cost reduction
- Validation via guardrails + eval
- Edge and cloud deployment
- Monitoring and alerting from day one
Results, proven in numbers.
These numbers are drawn from measurements across real AI projects. Every use case is different — but data-readiness discipline, production-focused MLOps, and continuous cost control make these outcomes repeatable. You work with teams certified on AWS Bedrock, Vertex AI, and Azure AI Foundry.
The right partner for the right AI project.
Our AI and machine learning service is designed for a specific profile of company. If any of the following describes you, you're in the right place.
- Technology leaders with a working PoC that can't reach production
- Product teams looking to bring LLM token costs under control
- Companies wanting to make use of their own data with RAG, agents, or fine-tuning
- Operations teams looking to put a computer vision or forecasting model into the field
- Data teams that need to stand up MLOps and monitoring infrastructure
Questions before you decide.
When the data is ready and the use case is clear, many well-scoped AI features go to production within 8 weeks. That window covers model build, MLOps setup, and monitoring. If data quality is low or the use case is highly layered, we'll give you an honest timeline estimate after discovery.
The reduction comes from a combination of techniques, not a single move: right-sizing model selection, prompt and context optimization, caching, routing to smaller models, and eliminating unnecessary calls. We measure current usage first, then set measurable targets and track the result together.
We constrain the risk starting from design: grounding responses in sources with RAG, guardrails, output validation, and continuous measurement with eval sets. No model can guarantee 100%, but in a measured and monitored system we keep the risk at an acceptable level.
LLM-based solutions (RAG, agents, fine-tuning), computer vision, natural language processing, recommendation systems, and forecasting models. We clarify together in use-case discovery which approach fits your problem; we don't answer every problem with AI — we focus on where it genuinely creates value.
Both are possible. We recommend edge, cloud, or hybrid deployment based on latency, data privacy, and cost requirements. We work with teams certified on AWS Bedrock, Vertex AI, and Azure AI Foundry, and we plan your data residency requirements at the start of architecture design.
